Sparsity-Driven Despeckling for SAR Images
Yazarlar (3)
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
Baha Sen
University Of Yildirim Beyazit, Türkiye
University Of Yildirim Beyazit, Türkiye
Fatih Nar Space And Defence Technologies (Sdt), Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | IEEE Geoscience and Remote Sensing Letters | ||
| Dergi ISSN | 1545-598X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2016 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2016 |
| Cilt / Sayı / Sayfa | 13 / 1 / 115–119 | DOI | 10.1109/LGRS.2015.2499445 |
| Makale Linki | https://ieeexplore.ieee.org/document/7339615 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Paralel ve Dağıtık Sistemler
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| Özet |
| Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the result of various SAR image processing tasks such as edge detection and segmentation. Thus, speckle reduction is critical and is used as a preprocessing step for smoothing homogeneous regions while preserving features such as edges and point scatterers. Although state-of-the-art methods provide better despeckling compared with conventional methods, their resource consumption is higher. In this letter, a sparsity-driven total-variation (TV) approach employing l0-norm, fractional norm, or l1-norm to smooth homogeneous regions with minimal degradation in edges and point scatterers is proposed. Proposed method, sparsity-driven despeckling (SDD), is capable of using different norms controlled by a single parameter and provides better or similar despeckling compared with the state-of-the-art methods with shorter execution … |
| Anahtar Kelimeler |
| Fractional norm | l0-norm | l1-norm | Speckle reduction | Synthetic aperture radar (SAR) | Total variation (TV) |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 42 |
| Scopus | 47 |
| Google Scholar | 60 |